code: def rank_candidates(query_rep, cands, length_penalty, dictionary):
      """
      Rank candidates given representation of query.
  
      :param query_rep:
          base query representation to match text again.
      :param cands:
          strings to compare against query_rep for matching tokens
      :param length_penalty:
          scores are divided by the norm taken to this power
      :dictionary:
          dictionary to use to tokenize text
  
      :returns:
          ordered list of candidate strings in score-ranked order
      """
      if True:
          mpq = MaxPriorityQueue(100)
          for c in cands:
              score = score_match(
                  query_rep, c, length_penalty, dictionary
              )
              mpq.add(c, score)
          return list(reversed(mpq))
      else:
          cands = list(cands)
          score = [0] * len(cands)
          for i, c in enumerate(cands):
              score[i] = -score_match(
                  query_rep, c, length_penalty, dictionary
              )
          r = [
              i[0] for i in sorted(enumerate(score), key=lambda x: x[1])
          ]
          res = []
          for i in range(min(100, len(score))):
              res.append(cands[r[i]])
          return res
  
imports: from typing import Optional
  from parlai.core.params import ParlaiParser
  from parlai.core.opt import Opt
  import math
  from collections.abc import Sequence
  import heapq
  import json
  import parlai.utils.torch
  from parlai.core.agents import Agent
  from parlai.core.dict import DictionaryAgent
  from parlai.utils.io import PathManager
label_desc: Use image node_id 2 for calling a global function with example usage: rank_candidates(query_rep, cands, length_penalty, dictionary) and returns: list, res